Model-Based Iterative Learning Control Applied to an Industrial Robot with Elasticity

In this paper model-based Iterative Learning Control (ILC) is applied to improve the tracking accuracy of an industrial robot with elasticity. The ILC algorithm iteratively updates the reference trajectory for the robot such that the predicted tracking error in the next iteration is minimised. The tracking error is predicted by a model of the closed-loop dynamics of the robot. The model includes the servo resonance frequency, the first resonance frequency caused by elasticity in the mechanism and the variation of both frequencies along the trajectory. Experimental results show that the tracking error of the robot can be reduced, even at frequencies beyond the first elastic resonance frequency

Sampling free iterative PCE filter for state and parameter estimation of nonlinear dynamical systems

We present a novel filter for state and parameter estimation in non-linear dynamical systems, based on a generalised Kalman filter formulation. To achieve a sampling-free implementation, polynomial chaos expansion (PCE) and a Galerkin projection method are utilized for the propagation of uncertainties through the system dynamics. The non-linear dynamics of the system are then linearised by a sequence of Gauss-Newton iterations in combination with linear Kalman updates. Additionally, we introduce a new square root implementation of the PCE-based filter. The proposed filter is evaluated on the Lorenz-63 and Lorenz-84 models for the task of simultaneous state and parameter estimation and is compared with two related approaches. Finally, the computational complexity of our square-root implementation is compared against two existing square root approaches.</p

Active Vibration Isolation Control: Comparison of Feedback and Feedforward Control Strategies Applied to Coriolis Mass-Flow Meters (I)

In this paper we describe the design, implementation and results of multi degree of freedom (DOF) active vibration control for a Coriolis mass-flow meter (CMFM). Without vibration control, environmental vibrational disturbances results in nanometre movement of the fluid-conveying tube which causes erroneous mass-flow measurements. In order to reduce the transmissibility from external vibrations to the internal tube displacement active vibration control is applied.\ud A comparison of a feedback control strategy (adding virtual mass and skyhook damping) and an adaptive feedforward control strategy is made, taking into account the sensor noise levels. Theoretic results are validated with a multi-DOF experimental setup, showing up to 40 dB reduction of the influence of external vibrations. The amount of reduction is limited by the sensor noise levels